OpenRaiser/Intern-Atlas huggingface dataset:
A Frequency-aware Software Cache for Large Recommendation System Embeddings
Venue: arXiv cs.IR 2022
Year: 2022

Deep learning recommendation models (DLRMs) have been widely applied in Internet companies. The embedding tables of DLRMs are too large to fit on GPU memory entirely. We propose a GPU-based software cache approaches to dynamically manage the embedding table in the CPU and GPU memory space by leveraging the id's frequency statistics of the target dataset. Our proposed software cache is efficient in training entire DLRMs on GPU in a synchronized update manner. It is also scaled to multiple GPUs in combination with the widely used hybrid parallel training approaches. Evaluating our prototype system shows that we can keep only 1.5% of the embedding parameters in the GPU to obtain a decent end-to-end training speed.
Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach
Venue: arXiv cs.LG 2022
Year: 2022

Obesity is a global epidemic causing at least 2.8 million deaths per year. This complex disease is associated with significant socioeconomic burden, reduced work productivity, unemployment, and other social determinants of Health (SDoH) disparities. Objective: The objective of this study was to investigate the effects of SDoH on obesity prevalence among adults in Shelby County, Tennessee, USA using a geospatial machine-learning approach. Obesity prevalence was obtained from publicly available CDC 500 cities database while SDoH indicators were extracted from the U.S. Census and USDA. We examined the geographic distributions of obesity prevalence patterns using Getis-Ord Gi* statistics and calibrated multiple models to study the association between SDoH and adult obesity. Also, unsupervised machine learning was used to conduct grouping analysis to investigate the distribution of obesity prevalence and associated SDoH indicators. Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County. In the census tract, median household income, as well as the percentage of individuals who were black, home renters, living below the poverty level, fifty-five years or older, unmarried, and uninsured, had a significant association with adult obesity prevalence. The grouping analysis revealed disparities in obesity prevalence amongst disadvantaged neighborhoods. More research is needed that examines linkages between geographical location, SDoH, and chronic diseases. These findings, which depict a significantly higher prevalence of obesity within disadvantaged neighborhoods, and other geospatial information can be leveraged to offer valuable insights informing health decision-making and interventions that mitigate risk factors for increasing obesity prevalence.
Distributed Attestation Revocation in Self-Sovereign Identity
Venue: arXiv cs.DC 2022
Year: 2022

Self-Sovereign Identity (SSI) aspires to create a standardised identity layer for the Internet by placing citizens at the centre of their data, thereby weakening the grip of big tech on current digital identities. However, as millions of both physical and digital identities are lost annually, it is also necessary for SSIs to possibly be revoked to prevent misuse. Previous attempts at designing a revocation mechanism typically violate the principles of SSI by relying on central trusted components. This lack of a distributed revocation mechanism hampers the development of SSI. In this paper, we address this limitation and present the first fully distributed SSI revocation mechanism that does not rely on specialised trusted nodes. Our novel gossip-based propagation algorithm disseminates revocations throughout the network and provides nodes with a proof of revocation that enables offline verification of revocations. We demonstrate through simulations that our protocol adequately scales to national levels.
Analysis of lightweight cryptographic solutions for authentication in IoT
Venue: arXiv cs.CR 2022
Year: 2022

Currently, special attention is being paid to scenarios where the interconnection of devices with heterogeneous computational and communication capabilities it is required. It is essential to integrate security services during the stages of design and deployment of these networks since many of these scenarios provide critical services such as medical health, payment systems, military affairs, access control, e-banking, etc. This work analyses several cryptographic primitives related to entity authentication providing robust solutions according to device capabilities.
Listen to Users, but Only 85% of the Time: How Black Swans Can Save Innovation in a Data-Driven World
Venue: arXiv cs.HC 2022
Year: 2022

Data-driven design is a proven success factor that more and more digital businesses embrace. At the same time, academics and practitioners alike warn that when virtually everything must be tested and proven with numbers, that can stifle creativity and innovation. This article argues that Taleb's Black Swan theory can solve this dilemma. It shows that online experimentation, and therefore digital design, are fat-tailed phenomena and, hence, prone to Black Swans. It introduces the notion of Black Swan designs -- "crazy" designs that make sense only in hindsight -- along with four specific criteria. To ensure incremental improvements and their potential for innovation, businesses should apply Taleb's barbell strategy: Invest 85-90% of resources into data-driven approaches and 10-15% into potential Black Swans.
A Sublinear Adversarial Training Algorithm
Venue: arXiv cs.LG 2022
Year: 2022

Adversarial training is a widely used strategy for making neural networks resistant to adversarial perturbations. For a neural network of width $m$, $n$ input training data in $d$ dimension, it takes $Ω(mnd)$ time cost per training iteration for the forward and backward computation. In this paper we analyze the convergence guarantee of adversarial training procedure on a two-layer neural network with shifted ReLU activation, and shows that only $o(m)$ neurons will be activated for each input data per iteration. Furthermore, we develop an algorithm for adversarial training with time cost $o(m n d)$ per iteration by applying half-space reporting data structure.
TagTeam: Towards Wearable-Assisted, Implicit Guidance for Human--Drone Teams
Venue: arXiv cs.HC 2022
Year: 2022

The availability of sensor-rich smart wearables and tiny, yet capable, unmanned vehicles such as nano quadcopters, opens up opportunities for a novel class of highly interactive, attention-shared human--machine teams. Reliable, lightweight, yet passive exchange of intent, data and inferences within such human--machine teams make them suitable for scenarios such as search-and-rescue with significantly improved performance in terms of speed, accuracy and semantic awareness. In this paper, we articulate a vision for such human--drone teams and key technical capabilities such teams must encompass. We present TagTeam, an early prototype of such a team and share promising demonstration of a key capability (i.e., motion awareness).
A Reflection on the Organic Growth of the Internet Protocol Stack
Venue: arXiv cs.NI 2022
Year: 2022

In the last 15 years, the Internet architecture has continued evolving organically, introducing new headers and protocols to the classic TCP/IP stack. More specifically, we have identified two major trends. First, it is common that most communications are encrypted, either at L3 or L4. And second, due to protocol ossification, developers have resorted to upper layers to introduce new functionalities (L4 and above). For example, QUIC's connection migration feature provides mobility at L4. In this paper we present a reflection around these changes, and attempt to formalize them by adding two additional protocol headers to the TCP/IP stack: one for security, and another for new functionalities. We must note that we are not presenting a new architecture, but trying to draw up what it's already out there. In addition, we elaborate on the forces that have brought us here, and we enumerate current proposals that are shaping these new headers. We also analyze in detail three examples of such trends: the Zero Trust Networking paradigm, the QUIC transport protocol, and modern SD-WAN systems. Finally, we present a formalization of this architecture by adding these two additional layers to the TCP/IP protocol stack. Our goal is triggering a discussion on the changes of the current Internet architecture.
Block Double-Submission Attack: Block Withholding Can Be Self-Destructive
Venue: arXiv cs.CR 2022
Year: 2022

Proof-of-Work (PoW) is a Sybil control mechanism adopted in blockchain-based cryptocurrencies. It prevents the attempt of malicious actors to manipulate distributed ledgers. Bitcoin has successfully suppressed double-spending by accepting the longest PoW chain. Nevertheless, PoW encountered several major security issues surrounding mining competition. One of them is a Block WithHolding (BWH) attack that can exploit a widespread and cooperative environment called a mining pool. This attack takes advantage of untrustworthy relationships between mining pools and participating agents. Moreover, detecting or responding to attacks is challenging due to the nature of mining pools. In this paper, however, we suggest that BWH attacks also have a comparable trust problem. Because a BWH attacker cannot have complete control over BWH agents, they can betray the belonging mining pool and seek further benefits by trading with victims. We prove that this betrayal is not only valid in all attack parameters but also provides double benefits; finally, it is the best strategy for BWH agents. Furthermore, our study implies that BWH attacks may encounter self-destruction of their own revenue, contrary to their intention.
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming
Venue: arXiv cs.IT 2022
Year: 2022

Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. In particular, subarray hybrid beamforming can further decrease power consumption by reducing the number of phase-shifters. However, designing the hybrid beamforming vectors is a complex task due to the discrete nature of the subarray connections and the phase-shift amounts. Finding the optimal connections between RF chains and antennas requires solving a non-convex problem in a large search space. In addition, conventional solutions assume that perfect CSI is available, which is not the case in practical systems. Therefore, we propose a novel unsupervised learning approach to design the hybrid beamforming for any subarray structure while supporting quantized phase-shifters and noisy CSI. One major feature of the proposed architecture is that no beamforming codebook is required, and the neural network is trained to take into account the phase-shifter quantization. Simulation results show that the proposed deep learning solutions can achieve higher sum-rates than existing methods.
CoditT5: Pretraining for Source Code and Natural Language Editing
Venue: arXiv cs.SE 2022
Year: 2022

Pretrained language models have been shown to be effective in many software-related generation tasks; however, they are not well-suited for editing tasks as they are not designed to reason about edits. To address this, we propose a novel pretraining objective which explicitly models edits and use it to build CoditT5, a large language model for software-related editing tasks that is pretrained on large amounts of source code and natural language comments. We fine-tune it on various downstream editing tasks, including comment updating, bug fixing, and automated code review. By outperforming standard generation-based models, we demonstrate the generalizability of our approach and its suitability for editing tasks. We also show how a standard generation model and our edit-based model can complement one another through simple reranking strategies, with which we achieve state-of-the-art performance for the three downstream editing tasks.
Poster: Revocation in VANETs Based on k-ary Huffman Trees
Venue: arXiv cs.CR 2022
Year: 2022

One of the biggest problems of vehicular ad-hoc networks is revocation. The efficient management of such issue has become one of the major paradigms in this area of research. A solution proposed here is based on the use of authenticated data structures like revocation trees to replace the classical and inefficient certificate revocation lists. In particular, the idea of this paper is to propose the use of k-ary hash trees, Huffman coding and a duplex version of the SHA-3 hash function, to optimize insertions and searches in the revocation structure. Thus, the inclusion of a new certificate revoked in the tree, only implies a new iteration of the duplex construction of the hash function, avoiding recalculating the entire hashes and the entire tree. Furthermore, a k-ary Huffman tree is used to insert leaf nodes at different levels so that those revoked nodes that are more queried, are located closer to the root node position, so the revocation proof is smaller for those vehicles that spend more time on the roads. This paper details a method to calculate the optimum value $k$ for the k-ary tree in order to optimize the revocation proof size. Therefore, the proposal described here improves both the insertion of new revoked certificates in the revocation structure and the search of revoked certificates in the revocation structure. This paper is part of a work in progress, so that we plan to implement the scheme in real scenarios to get ideal values of the parameters and comparisons with other schemes.
Semi-supervised segmentation of tooth from 3D Scanned Dental Arches
Venue: arXiv cs.CV 2022
Year: 2022

Teeth segmentation is an important topic in dental restorations that is essential for crown generation, diagnosis, and treatment planning. In the dental field, the variability of input data is high and there are no publicly available 3D dental arch datasets. Although there has been improvement in the field provided by recent deep learning architectures on 3D data, there still exists some problems such as properly identifying missing teeth in an arch. We propose to use spectral clustering as a self-supervisory signal to joint-train neural networks for segmentation of 3D arches. Our approach is motivated by the observation that K-means clustering provides cues to capture margin lines related to human perception. The main idea is to automatically generate training data by decomposing unlabeled 3D arches into segments relying solely on geometric information. The network is then trained using a joint loss that combines a supervised loss of annotated input and a self-supervised loss of non-labeled input. Our collected data has a variety of arches including arches with missing teeth. Our experimental results show improvement over the fully supervised state-of-the-art MeshSegNet when using semi-supervised learning. Finally, we contribute code and a dataset.
Comparing Channel Restrictions of Communicating State Machines, High-level Message Sequence Charts, and Multiparty Session Types
Venue: arXiv cs.FL 2022
Year: 2022

Communicating state machines provide a formal foundation for distributed computation. Unfortunately, they are Turing-complete and, thus, challenging to analyse. In this paper, we classify restrictions on channels which have been proposed to work around the undecidability of verification questions. We compare half-duplex communication, existential B-boundedness, and k-synchronisability. These restrictions do not prevent the communication channels from growing arbitrarily large but still restrict the power of the model. Each restriction gives rise to a set of languages so, for every pair of restrictions, we check whether one subsumes the other or if they are incomparable. We investigate their relationship in two different contexts: first, the one of communicating state machines, and, second, the one of communication protocol specifications using high-level message sequence charts. Surprisingly, these two contexts yield different conclusions. In addition, we integrate multiparty session types, another approach to specify communication protocols, into our classification. We show that multiparty session type languages are half-duplex, existentially 1-bounded, and 1-synchronisable. To~show this result, we provide the first formal embedding of multiparty session types into high-level message sequence charts.
What's on your mind? A Mental and Perceptual Load Estimation Framework towards Adaptive In-vehicle Interaction while Driving
Venue: arXiv cs.HC 2022
Year: 2022

Several researchers have focused on studying driver cognitive behavior and mental load for in-vehicle interaction while driving. Adaptive interfaces that vary with mental and perceptual load levels could help in reducing accidents and enhancing the driver experience. In this paper, we analyze the effects of mental workload and perceptual load on psychophysiological dimensions and provide a machine learning-based framework for mental and perceptual load estimation in a dual task scenario for in-vehicle interaction (https://github.com/amrgomaaelhady/MWL-PL-estimator). We use off-the-shelf non-intrusive sensors that can be easily integrated into the vehicle's system. Our statistical analysis shows that while mental workload influences some psychophysiological dimensions, perceptual load shows little effect. Furthermore, we classify the mental and perceptual load levels through the fusion of these measurements, moving towards a real-time adaptive in-vehicle interface that is personalized to user behavior and driving conditions. We report up to 89% mental workload classification accuracy and provide a real-time minimally-intrusive solution.
Diamonds are Forever in the Blockchain: Geometric Polyhedral Point-Set Pattern Matching
Venue: arXiv cs.CG 2022
Year: 2022

Motivated by blockchain technology for supply-chain tracing of ethically sourced diamonds, we study geometric polyhedral point-set pattern matching as minimum-width polyhedral annulus problems under translations and rotations. We provide two $(1 + \varepsilon)$-approximation schemes under translations with $O(\varepsilon^{-d} n)$-time for $d$ dimensions and $O(n\log \varepsilon^{-1} + \varepsilon^{-2})$-time for two dimensions, and we give an $O(f^{d-1}\varepsilon^{1-2d}n)$-time algorithm when also allowing for rotations, parameterized on $f$, which we define as the slimness of the point set.
Symbolic Music Loop Generation with Neural Discrete Representations
Venue: arXiv cs.SD 2022
Year: 2022

Since most of music has repetitive structures from motifs to phrases, repeating musical ideas can be a basic operation for music composition. The basic block that we focus on is conceptualized as loops which are essential ingredients of music. Furthermore, meaningful note patterns can be formed in a finite space, so it is sufficient to represent them with combinations of discrete symbols as done in other domains. In this work, we propose symbolic music loop generation via learning discrete representations. We first extract loops from MIDI datasets using a loop detector and then learn an autoregressive model trained by discrete latent codes of the extracted loops. We show that our model outperforms well-known music generative models in terms of both fidelity and diversity, evaluating on random space. Our code and supplementary materials are available at https://github.com/sjhan91/Loop_VQVAE_Official.
SSLEM: A Simplifier for MBA Expressions based on Semi-linear MBA Expressions and Program Synthesis
Venue: arXiv cs.CR 2022
Year: 2022

MBA (mixed boolean and arithmetic) expressions are hard to simplify, so used for malware obfuscation to hinder analysts' diagnosis. Some MBA simplification methods with high performance have been developed, but they narrowed the target to "linear" MBA expressions, which allows efficient solutions based on logic/term-rewriting. However such restrictions are not appropriate for general forms of MBA expressions usually appearing in malware. To overcome this limitation, we introduce a "semi-linear" MBA expression, a new class of MBA expression extended from a linear MBA expression, and propose a new MBA simplifier called "SSLEM", based on a simplification idea of semi-linear MBA expressions and program synthesis
ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design
Venue: arXiv cs.CV 2022
Year: 2022

Cross-modal fashion image synthesis has emerged as one of the most promising directions in the generation domain due to the vast untapped potential of incorporating multiple modalities and the wide range of fashion image applications. To facilitate accurate generation, cross-modal synthesis methods typically rely on Contrastive Language-Image Pre-training (CLIP) to align textual and garment information. In this work, we argue that simply aligning texture and garment information is not sufficient to capture the semantics of the visual information and therefore propose MaskCLIP. MaskCLIP decomposes the garments into semantic parts, ensuring fine-grained and semantically accurate alignment between the visual and text information. Building on MaskCLIP, we propose ARMANI, a unified cross-modal fashion designer with part-level garment-text alignment. ARMANI discretizes an image into uniform tokens based on a learned cross-modal codebook in its first stage and uses a Transformer to model the distribution of image tokens for a real image given the tokens of the control signals in its second stage. Contrary to prior approaches that also rely on two-stage paradigms, ARMANI introduces textual tokens into the codebook, making it possible for the model to utilize fine-grain semantic information to generate more realistic images. Further, by introducing a cross-modal Transformer, ARMANI is versatile and can accomplish image synthesis from various control signals, such as pure text, sketch images, and partial images. Extensive experiments conducted on our newly collected cross-modal fashion dataset demonstrate that ARMANI generates photo-realistic images in diverse synthesis tasks and outperforms existing state-of-the-art cross-modal image synthesis approaches.Our code is available at https://github.com/Harvey594/ARMANI.
Quantized Adaptive Subgradient Algorithms and Their Applications
Venue: arXiv cs.LG 2022
Year: 2022

Data explosion and an increase in model size drive the remarkable advances in large-scale machine learning, but also make model training time-consuming and model storage difficult. To address the above issues in the distributed model training setting which has high computation efficiency and less device limitation, there are still two main difficulties. On one hand, the communication costs for exchanging information, e.g., stochastic gradients among different workers, is a key bottleneck for distributed training efficiency. On the other hand, less parameter model is easy for storage and communication, but the risk of damaging the model performance. To balance the communication costs, model capacity and model performance simultaneously, we propose quantized composite mirror descent adaptive subgradient (QCMD adagrad) and quantized regularized dual average adaptive subgradient (QRDA adagrad) for distributed training. To be specific, we explore the combination of gradient quantization and sparse model to reduce the communication cost per iteration in distributed training. A quantized gradient-based adaptive learning rate matrix is constructed to achieve a balance between communication costs, accuracy, and model sparsity. Moreover, we theoretically find that a large quantization error brings in extra noise, which influences the convergence and sparsity of the model. Therefore, a threshold quantization strategy with a relatively small error is adopted in QCMD adagrad and QRDA adagrad to improve the signal-to-noise ratio and preserve the sparsity of the model. Both theoretical analyses and empirical results demonstrate the efficacy and efficiency of the proposed algorithms.